Correlated Hierarchical Autoregressive Models Image Compression | Zendy

Ghadah Al Khafaji | Zendy; Taha Mohammed Hasan | Zendy; Salam Noaman | Zendy

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Correlated Hierarchical Autoregressive Models Image Compression

Author(s) -

Ghadah Al Khafaji,

Taha Mohammed Hasan,

Salam Noaman

Publication year - 2017

Publication title -

diyala journal for pure science

Language(s) - English

Resource type - Journals

eISSN - 2518-9255

pISSN - 2222-8373

DOI - 10.24237/djps.1303.178c

Subject(s) - autoregressive model , star model , image compression , image (mathematics) , computer science , compression (physics) , artificial intelligence , econometrics , mathematics , autoregressive integrated moving average , image processing , time series , machine learning , materials science , composite material

In this paper, a Correlated Hierarchical Autoregressive Model (CHARM) method for image compression is proposed. It based on using multi-layered modeling concept of correlated autoregressive coefficients, which is a modified version of Hierarchical Autoregressive Model (HARM). The test results indicate that the suggested techniques improve the compression ratio along with preserving the image quality compared to traditional predictive coding or autoregressive model and HARM on a series of selected images.

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